The rhythm of electroencephalogram (EEG) depends on the neuroanatomical-based parameters such as white matter (WM) connectivity. However, the impacts of these parameters on the specific characteristics of EEG have not been clearly understood. Previous studies demonstrated that, these parameters contribute the inter-subject differences of EEG during performance of specific task such as motor imagery (MI). Though researchers have worked on this phenomenon, the idea is yet to be understood in terms of the mechanism that underlies such differences. Here, to tackle this issue, we began our investigations by first examining the structural features related to scalp EEG characteristics, which are event-related desynchronizations (ERDs), during MI using diffusion MRI. Twenty-four right-handed subjects were recruited to accomplish MI tasks and MRI scans. Based on the high spatial resolution of the structural and diffusion images, the motor-related WM links, such as basal ganglia (BG)-primary somatosensory cortex (SM1) pathway and supplementary motor area (SMA)-SM1 connection, were reconstructed by using probabilistic white matter tractography. Subsequently, the relationships of WM characteristics with EEG signals were investigated. These analyses demonstrated that WM pathway characteristics, including the connectivity strength and the positional characteristics of WM connectivity on SM1 (defined by the gyrus-sulcus ratio of connectivity, GSR), have a significant impact on ERDs when doing MI. Interestingly, the high GSR of WM connections between SM1 and BG were linked to the better ERDs. These results therefore, indicated that the connectivity in the gyrus of SM1 interacted with MI network which played the critical role for the scalp EEG signal extraction of MI to a great extent. The study provided the coupling mechanism between structural and dynamic physiological features of human brain, which would also contribute to understanding individual differences of EEG in MI-brain computer interface.
Diffusion MRI Eeg White matter connectivity Motor imagery Brain-computer interface
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This study was funded by grants from the National Nature Science Foundation of China (81330032), the PCSIRT Project (IRT0910), and Special-Funded Program on National Key Scientific Instruments and Equipment Development of China (2013YQ49085908).
Compliance with Ethical Standards
None of the authors has any conflict of interest to disclose. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.
Boecker H, Dagher A, Ceballos-Baumann AO et al (1998) Role of the human rostral supplementary motor area and the basal ganglia in motor sequence control: investigations with H2 15O PET. J Neurophysiol 79:1070–1080PubMedGoogle Scholar
Malouin F, Richards CL, Jackson PL et al (2007) The kinesthetic and visual imagery questionnaire (KVIQ) for assessing motor imagery in persons with physical disabilities: a reliability and construct validity study. J Neurol Phys Ther 31:20–29. doi:10.1097/01.NPT.0000260567.24122.64CrossRefPubMedGoogle Scholar
Pfurtscheller G, Neuper C (1997) Motor imagery activates primary sensorimotor area in humans. Neurosci Lett 239:65–68CrossRefPubMedGoogle Scholar
Postle BR, D’Esposito M (1999) Dissociation of human caudate nucleus activity in spatial and nonspatial working memory: an event-related fMRI study. Brain Res Cogn Brain Res 8:107–115CrossRefPubMedGoogle Scholar
Ruschel M, Knosche TR, Friederici AD et al (2013) Connectivity architecture and subdivision of the human inferior parietal cortex revealed by diffusion MRI. Cereb Cortex. doi:10.1093/cercor/bht098PubMedGoogle Scholar
Villablanca JR (2010) Why do we have a caudate nucleus. Acta Neurobiol Exp 70:95–105Google Scholar
Whitford TJ, Rennie CJ, Grieve SM et al (2006) Brain maturation in adolescence: concurrent changes in neuroanatomy and neurophysiology. Hum Brain Mapp 28:228–237. doi:10.1002/hbm.20273CrossRefGoogle Scholar
Zhang R, Xu P, Chen R et al (2015) Predicting inter-session performance of SMR-Based brain–computer interface using the spectral entropy of resting-state EEG. Brain Topogr. doi:10.1007/s10548-015-0429-3Google Scholar
1.Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina